Water Retrieval Embedded Attention Network With Multiscale Receptive Fields for Hyperspectral Image Refined Classification

In hyperspectral image classification, deep learning (DL) based on abundant training samples has demonstrated its significance in classification performance. However, due to the limitation of available samples and the imbalance/similarity of classes in small-sized datasets, data-driven DL algorithms can hardly extract representative and effective features for interclass classification, and the subtle diagnostic spectral features for intraclass classification are easily covered or lost in the iterative feature extraction (FE). The restricted FE of interclass/intraclass results in the accuracy reduction and performance limitation of refined classification. To mitigate these issues, an attention network with multiscale receptive fields (MRFs) is proposed, embedding an inversion subnet for relative water content retrieval (RWCR). In classification, the three critical parts in the proposed network, namely, MRFs, embedded subnet, and multiple-attention mechanism, are responsible for multiscale feature merging, relative water content (RWC) feature enhancement, and paying attention to bands, channels, and multiscale features, respectively. The ablation studies on small-sized datasets show the accuracy improvements of interclass and intraclass in refined classification, which verifies the effectiveness of critical parts for extracting representative features and taking RWC features as the diagnostic biochemical signature from unbalanced and similar classes. The comparison results with typical DL models demonstrate the superiority of the proposed network. Moreover, the competitive advantage of the proposed network is demonstrated in comparison with traditional and state-of-the-art HSI classification methods.